Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 4 Articles
We propose a new watermarking method based on quantization index modulation. A concept of initial data loss is introduced in\norder to increase capacity of thewatermarking channel under high intensity additivewhiteGaussian noise.According to the concept\nsome samples in predefined positions are ignored even though this produces errors in the initial stage of watermark embedding.\nThe proposed method also exploits a new form of distribution of quantized samples where samples that interpret ââ?¬Å?0ââ?¬Â and ââ?¬Å?1ââ?¬Â have\ndifferently shaped probability density functions. Compared to well-known watermarking schemes, this provides an increase of\ncapacity under noise attack and introduces a distinctive feature. Two criteria are proposed that express the feature numerically. The\ncriteria are utilized by a procedure for estimation of a gain factor after possible gain attack. Several state-of-the-art quantizationbased\nwatermarking methods were used for comparison on a set of natural grayscale images. The superiority of the proposed\nmethod has been confirmed for different types of popular attacks....
This paper proposes a tree-based adaptive broadcasting (TAB) algorithm for data dissemination to improve data access efficiency.\nThe proposed TAB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data and splits the\nbroadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model\nto derive the mean access latency of the generated broadcast program. In light of the derived results, both the index channel�s\nbandwidth and the data channel�s bandwidth can be optimally allocated to maximize bandwidth utilization. This paper presents\nexperiments to help evaluate the effectiveness of the proposed strategy. From the experimental results, it can be seen that the\nproposed mechanism is feasible in practice....
Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the\nmethods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for\nWMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers\nthe target is defined by the deflection angle between target and the sensor�s current orientation and the distance between target\nand the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor\nsingle-target, multi sensor single-target, and single-sensor multi targets problems distinguishingly. Selecting the orientation that\nsensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to\nmulti sensor multi targets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors\nwhich covers all the targets in networks. Simulation results show the algorithm�s performance and the effect of number of targets\non the resulting subset....
The need for adapting video stream delivery over heterogeneous and unreliable networks requires self-adaptive and error resilient\ncoding.Network bandwidth fluctuations can be handled by means of a video coding scheme which adapts to the channel conditions.\nHowever, packet losses which are frequent in wireless networks can cause a mismatch during the reconstruction in the receiver end\nand result in an accumulation of errors which deteriorates the quality of the delivered video. A combination of multiple description\ncoding in pixel domain and scalable video coding schemes which addresses both video adaptation and robustness to data loss\nis proposed in this paper. The proposed scheme combines error concealment with spatial video scalability. In order to improve\nthe fidelity of the reconstructed to the original frames in presence of packet loss, a multilayer poly phase spatial decomposition\nalgorithm is proposed. Classica multiple description methods interpolate the missing data which results in smoothing and artifact\nat object boundaries. The proposed algorithm addresses the quality degradation due to low-pass filtering effect of interpolation\nmethods.We also comparatively analyze the trade-off between robustness to channel errors and coding efficiency....
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